Whole-genome CRISPR screening identifies genetic manipulations to reduce immune rejection of stem cell-derived islets.

Human embryonic stem cells (hESCs) provide opportunities for cell replacement therapy of insulin-dependent diabetes. Therapeutic quantities of human stem cell-derived islets (SC-islets) can be produced by directed differentiation. However, preventing allo-rejection and recurring autoimmunity, without the use of encapsulation or systemic immunosuppressants, remains a challenge. An attractive approach is to transplant SC-islets, genetically modified to reduce the impact of immune rejection. To determine the underlying forces that drive immunogenicity of SC-islets in inflammatory environments, we performed single-cell RNA sequencing (scRNA-seq) and whole-genome CRISPR screen of SC-islets under immune interaction with allogeneic peripheral blood mononuclear cells (PBMCs). Data analysis points to "alarmed" populations of SC-islets that upregulate genes in the interferon (IFN) pathway. The CRISPR screen in vivo confirms that targeting IFNγ-induced mediators has beneficial effects on SC-islet survival under immune attack. Manipulating the IFN response by depleting chemokine ligand 10 (CXCL10) in SC-islet grafts confers improved survival against allo-rejection compared with wild-type grafts in humanized mice. These results offer insights into the nature of immune destruction of SC-islets during allogeneic responses and provide targets for gene editing.

Causal Genetic Variation Underlying Metabolome Differences.

An ongoing challenge in biology is to predict the phenotypes of individuals from their genotypes. Genetic variants that cause disease often change an individual's total metabolite profile, or metabolome. In light of our extensive knowledge of metabolic pathways, genetic variants that alter the metabolome may help predict novel phenotypes. To link genetic variants to changes in the metabolome, we studied natural variation in the yeast Saccharomyces cerevisiae We used an untargeted mass spectrometry method to identify dozens of metabolite Quantitative Trait Loci (mQTL), genomic regions containing genetic variation that control differences in metabolite levels between individuals. We mapped differences in urea cycle metabolites to genetic variation in specific genes known to regulate amino acid biosynthesis. Our functional assays reveal that genetic variation in two genes, AUA1 and ARG81, cause the differences in the abundance of several urea cycle metabolites. Based on knowledge of the urea cycle, we predicted and then validated a new phenotype: sensitivity to a particular class of amino acid isomers. Our results are a proof-of-concept that untargeted mass spectrometry can reveal links between natural genetic variants and metabolome diversity. The interpretability of our results demonstrates the promise of using genetic variants underlying natural differences in the metabolome to predict novel phenotypes from genotype.

Bar Coding MS(2) Spectra for Metabolite Identification.

Metabolite identifications are most frequently achieved in untargeted metabolomics by matching precursor mass and full, high-resolution MS(2) spectra to metabolite databases and standards. Here we considered an alternative approach for establishing metabolite identifications that does not rely on full, high-resolution MS(2) spectra. First, we select mass-to-charge regions containing the most informative metabolite fragments and designate them as bins. We then translate each metabolite fragmentation pattern into a binary code by assigning 1's to bins containing fragments and 0's to bins without fragments. With 20 bins, this binary-code system is capable of distinguishing 96% of the compounds in the METLIN MS(2) library. A major advantage of the approach is that it extends untargeted metabolomics to low-resolution triple quadrupole (QqQ) instruments, which are typically less expensive and more robust than other types of mass spectrometers. We demonstrate a method of acquiring MS(2) data in which the third quadrupole of a QqQ instrument cycles over 20 wide isolation windows (coinciding with the location and width of our bins) for each precursor mass selected by the first quadrupole. Operating the QqQ instrument in this mode yields diagnostic bar codes for each precursor mass that can be matched to the bar codes of metabolite standards. Furthermore, our data suggest that using low-resolution bar codes enables QqQ instruments to make MS(2)-based identifications in untargeted metabolomics with a specificity and sensitivity that is competitive to high-resolution time-of-flight technologies.

Using whole-genome sequences of the LG/J and SM/J inbred mouse strains to prioritize quantitative trait genes and nucleotides.

BACKGROUND: The laboratory mouse is the most commonly used model for studying variation in complex traits relevant to human disease. Here we present the whole-genome sequences of two inbred strains, LG/J and SM/J, which are frequently used to study variation in complex traits as diverse as aging, bone-growth, adiposity, maternal behavior, and methamphetamine sensitivity. RESULTS: We identified small nucleotide variants (SNVs) and structural variants (SVs) in the LG/J and SM/J strains relative to the reference genome and discovered novel variants in these two strains by comparing their sequences to other mouse genomes. We find that 39% of the LG/J and SM/J genomes are identical-by-descent (IBD). We characterized amino-acid changing mutations using three algorithms: LRT, PolyPhen-2 and SIFT. We also identified polymorphisms between LG/J and SM/J that fall in regulatory regions and highly informative transcription factor binding sites (TFBS). We intersected these functional predictions with quantitative trait loci (QTL) mapped in advanced intercrosses of these two strains. We find that QTL are both over-represented in non-IBD regions and highly enriched for variants predicted to have a functional impact. Variants in QTL associated with metabolic (231 QTL identified in an F16 generation) and developmental (41 QTL identified in an F34 generation) traits were interrogated and we highlight candidate quantitative trait genes (QTG) and nucleotides (QTN) in a QTL on chr13 associated with variation in basal glucose levels and in a QTL on chr6 associated with variation in tibia length. CONCLUSIONS: We show how integrating genomic sequence with QTL reduces the QTL search space and helps researchers prioritize candidate genes and nucleotides for experimental follow-up. Additionally, given the LG/J and SM/J phylogenetic context among inbred strains, these data contribute important information to the genomic landscape of the laboratory mouse.

Discriminating precursors of common fragments for large-scale metabolite profiling by triple quadrupole mass spectrometry.

MOTIVATION: The goal of large-scale metabolite profiling is to compare the relative concentrations of as many metabolites extracted from biological samples as possible. This is typically accomplished by measuring the abundances of thousands of ions with high-resolution and high mass accuracy mass spectrometers. Although the data from these instruments provide a comprehensive fingerprint of each sample, identifying the structures of the thousands of detected ions is still challenging and time intensive. An alternative, less-comprehensive approach is to use triple quadrupole (QqQ) mass spectrometry to analyze predetermined sets of metabolites (typically fewer than several hundred). This is done using authentic standards to develop QqQ experiments that specifically detect only the targeted metabolites, with the advantage that the need for ion identification after profiling is eliminated. RESULTS: Here, we propose a framework to extend the application of QqQ mass spectrometers to large-scale metabolite profiling. We aim to provide a foundation for designing QqQ multiple reaction monitoring (MRM) experiments for each of the 82 696 metabolites in the METLIN metabolite database. First, we identify common fragmentation products from the experimental fragmentation data in METLIN. Then, we model the likelihoods of each precursor structure in METLIN producing each common fragmentation product. With these likelihood estimates, we select ensembles of common fragmentation products that minimize our uncertainty about metabolite identities. We demonstrate encouraging performance and, based on our results, we suggest how our method can be integrated with future work to develop large-scale MRM experiments. AVAILABILITY AND IMPLEMENTATION: Our predictions, Supplementary results, and the code for estimating likelihoods and selecting ensembles of fragmentation reactions are made available on the lab website at http://pattilab.wustl.edu/FragPred.

X13CMS: global tracking of isotopic labels in untargeted metabolomics.

Studies of isotopically labeled compounds have been fundamental to understanding metabolic pathways and fluxes. They have traditionally, however, been used in conjunction with targeted analyses that identify and quantify a limited number of labeled downstream metabolites. Here we describe an alternative workflow that leverages recent advances in untargeted metabolomic technologies to track the fates of isotopically labeled metabolites in a global, unbiased manner. This untargeted approach can be applied to discover novel biochemical pathways and characterize changes in the fates of labeled metabolites as a function of altered biological conditions such as disease. To facilitate the data analysis, we introduce X(13)CMS, an extension of the widely used mass spectrometry-based metabolomic software package XCMS. X(13)CMS uses the XCMS platform to detect metabolite peaks and perform retention-time alignment in liquid chromatography/mass spectrometry (LC/MS) data. With the use of the XCMS output, the program then identifies isotopologue groups that correspond to isotopically labeled compounds. The retrieval of these groups is done without any a priori knowledge besides the following input parameters: (i) the mass difference between the unlabeled and labeled isotopes, (ii) the mass accuracy of the instrument used in the analysis, and (iii) the estimated retention-time reproducibility of the chromatographic method. Despite its name, X(13)CMS can be used to track any isotopic label. Additionally, it detects differential labeling patterns in biological samples collected from parallel control and experimental conditions. We validated the ability of X(13)CMS to accurately retrieve labeled metabolites from complex biological matrices both with targeted LC/MS/MS analysis of a subset of the hits identified by the program and with labeled standards spiked into cell extracts. We demonstrate the full functionality of X(13)CMS with an analysis of cultured rat astrocytes treated with uniformly labeled (U-)(13)C-glucose during lipopolysaccharide (LPS) challenge. Our results show that out of 223 isotopologue groups enriched from U-(13)C-glucose, 95 have statistically significant differential labeling patterns in astrocytes challenged with LPS compared to unchallenged control cells. Only two of these groups overlap with the 32 differentially regulated peaks identified by XCMS, indicating that X(13)CMS uncovers different and complementary information from untargeted metabolomic studies. Like XCMS, X(13)CMS is implemented in R. It is available from our laboratory website at http://pattilab.wustl.edu/x13cms.php .

An untargeted metabolomic workflow to improve structural characterization of metabolites.

Mass spectrometry-based metabolomics relies on MS(2) data for structural characterization of metabolites. To obtain the high-quality MS(2) data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here, we show that metabolomic MS(2) data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS(2) fragments, even narrow windows are not always selective enough, and they can complicate data analysis by removing isotopic patterns from MS(2) spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work, we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS(2) scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS(2) fragment intensities as a function of retention time and precursor mass targeted for MS(2) analysis, we obtain deconvolved MS(2) spectra that are consistent with pure standards and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic extracts from brain, liver, astrocytes, as well as nerve tissue, and performance is evaluated by using pure metabolite standards in combination with simulations based on raw MS(2) data from the METLIN metabolite database. A R package implementing the algorithms used in our workflow is available on our laboratory website ( http://pattilab.wustl.edu/decoms2.php ).

Evolution of the human gastrokine locus and confounding factors regarding the pseudogenicity of GKN3.

In a screen for genes expressed specifically in gastric mucous neck cells, we identified GKN3, the recently discovered third member of the gastrokine family. We present confirmatory mouse data and novel porcine data showing that mouse GKN3 expression is confined to mucous cells of the corpus neck and antrum base and is prominently expressed in metaplastic lesions. GKN3 was proposed originally to be expressed in some human populations and a pseudogene in others. To investigate that hypothesis, we studied human GKN3 evolution in the context of its paralogous genomic neighbors, GKN1 and GKN2. Haplotype analysis revealed that GKN3 mimics GKN2 in patterns of exonic SNP allocation, whereas GKN1 appeared to be more stringently selected. GKN3 showed signatures of both directional selection and population based selective sweeps in humans. One such selective sweep includes SNP rs10187256, originally identified as an ancestral tryptophan to premature STOP codon mutation. The derived (nonancestral) allele went to fixation in Asia. We show that another SNP, rs75578132, identified 5 bp downstream of rs10187256, exhibits a second selective sweep in almost all Europeans, some Latinos, and some Africans, possibly resulting from a reintroduction of European genes during African colonization. Finally, we identify a mutation that would destroy the splice donor site in the putative exon3-intron3 boundary, which occurs in all human genomes examined to date. Our results highlight a stomach-specific human genetic locus, which has undergone various selective sweeps across European, Asian, and African populations and thus reflects geographic and ethnic patterns in genome evolution.

A view from above: cloud plots to visualize global metabolomic data.

Global metabolomics describes the comprehensive analysis of small molecules in a biological system without bias. With mass spectrometry-based methods, global metabolomic data sets typically comprise thousands of peaks, each of which is associated with a mass-to-charge ratio, retention time, fold change, p-value, and relative intensity. Although several visualization schemes have been used for metabolomic data, most commonly used representations exclude important data dimensions and therefore limit interpretation of global data sets. Given that metabolite identification through tandem mass spectrometry data acquisition is a time-limiting step of the untargeted metabolomic workflow, simultaneous visualization of these parameters from large sets of data could facilitate compound identification and data interpretation. Here, we present such a visualization scheme of global metabolomic data using a so-called "cloud plot" to represent multidimensional data from septic mice. While much attention has been dedicated to lipid compounds as potential biomarkers for sepsis, the cloud plot shows that alterations in hydrophilic metabolites may provide an early signature of the disease prior to the onset of clinical symptoms. The cloud plot is an effective representation of global mass spectrometry-based metabolomic data, and we describe how to extract it as standard output from our XCMS metabolomic software.